class_docstr: Loss function for knowledge distilling using KL divergence.
  
  Args:
      reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
      loss_weight (float): Loss weight of current loss.
      T (int): Temperature for distillation.
code: def __init__(
      self,
      reduction: str = "mean",
      loss_weight: float = 1.0,
      T: int = 10,
  ) -> None:
      super().__init__()
      assert T >= 1
      self.reduction = reduction
      self.loss_weight = loss_weight
      self.T = T
  
file_path: mmdetection/mmdet/models/losses/kd_loss.py
imports: from typing import Optional
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from mmdet.registry import MODELS
  from .utils import weighted_loss
label_desc: Use image node_id 1 to create a new KnowledgeDistillationKLDivLoss object from inherited base classes: nn with example: obj = KnowledgeDistillationKLDivLoss(reduction, loss_weight, T)